Groupwise Geometric and Photometric Direct Image Registration

Image registration consists in estimating geometric and photometric transformations that align two images as best as possible. The direct approach consists in minimizing the discrepancy in the intensity or color of the pixels. The inverse compositional algorithm has been recently proposed for the direct estimation of groupwise geometric transformations. It is efficient in that it performs several computationally expensive calculations at a pre-computation phase. We propose the dual inverse compositional algorithm which deals with groupwise geometric and photometric transformations, the latter acting on the value of the pixels. Our algorithm preserves the efficient pre-computation based design of the original inverse compositional algorithm. Previous attempts at incorporating photometric transformations to the inverse compositional algorithm spoil this property. We demonstrate our algorithm on simulated and real data and show the improvement in computational efficiency compared to previous algorithms

Analyzing the precision of JSW measurements using 3D scans and statistical models

One of the methods to diagnose rheumatoid arthritis (RA) is measuring joint space narrowing over time. A method is presented to analyze the sensitivity of this measurement to positioning of the hand. Micro-CT scans are used to generate projections of a joint under varying angles of rotation. A semi-automatic method is used to measure the joint space width (JSW) for each projection. A Statistical model is used to investigate whether the rotation can be detected from a 2D radiograph. It is shown that rotation of the hand has a significant influence on the measured JSW

Shape-appearance-correlated active appearance model

© 2016 Elsevier Ltd Among the challenges faced by current active shape or appearance models, facial-feature localization in the wild, with occlusion in a novel face image, i.e. in a generic environment, is regarded as one of the most difficult computer-vision tasks. In this paper, we propose an Active Appearance Model (AAM) to tackle the problem of generic environment. Firstly, a fast face-model initialization scheme is proposed, based on the idea that the local appearance of feature points can be accurately approximated with locality constraints. Nearest neighbors, which have similar poses and textures to a test face, are retrieved from a training set for constructing the initial face model. To further improve the fitting of the initial model to the test face, an orthogonal CCA (oCCA) is employed to increase the correlation between shape features and appearance features represented by Principal Component Analysis (PCA). With these two contributions, we propose a novel AAM, namely the shape-appearance-correlated AAM (SAC-AAM), and the optimization is solved by using the recently proposed fast simultaneous inverse compositional (Fast-SIC) algorithm. Experiment results demonstrate a 5–10% improvement on controlled and semi-controlled datasets, and with around 10% improvement on wild face datasets in terms of fitting accuracy compared to other state-of-the-art AAM models